1. Field
One or more embodiments set forth in the following description relate to simultaneous localization and mapping (SLAM) which is a technique used by robots and, more particularly, to measurement updates of SLAM.
2. Description of the Related Art
A robot is a machine which looks like a human being and performs various complex acts of a human being. Nowadays, the term robot generally refers to an entity which is programmed to move and perform certain tasks autonomously.
In particular, mobile robots can perform tasks in place of a human being in an extreme situation or a dangerous region. Home appliance robots, such as a robot cleaner, have also been popularized.
The mobile robot typically desires a localization and mapping algorithm to move and perform certain tasks autonomously. Simultaneous localization and mapping (SLAM) is an exemplary localization and mapping technique used by robots to build up a map within an unknown environment while at the same time keeping track of their current position.
SLAM can use many different types of sensors to acquire data used in building the map. Since the data from such sensors may contain errors, SLAM involves recursive data processing to obtain more reliable values based on the data from the sensors. For instance, SLAM reduces errors in positions of a robot or errors in acquired data through measurement updates.
The measurement updates update a calculated value in a previous step by comparing a measured value with the calculated value. The comparison between the measured value and the calculated value is typically performed in a two-dimensional space. Accordingly, SLAM involves a conversion operation from three-dimensional data about positions of a robot and the environment surrounding the robot in a three dimensional space into two-dimensional data in a two-dimensional space. However, since the conversion process includes a number of non-linear components, the operation slows down the overall processing speed, resulting in the SLAM performance being deteriorated.